20091110 Technical Seminar ChIP-seq Data Analysis

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    20091110 Technical Seminar ChIP-seq Data Analysis - Presentation Transcript

    1. Tools and challenges for ChIP-seq data analysis Alba Jené Sanz Biomedical Genomics Lab (UPF)
    2. Overview 1. ChIP-seq – The basics 2. Typical pipeline 3. Challenges in ChIP-seq data analysis 4. To take into account 5. Available tools 6. Analysis example 7. Future Challenges 8. Where to look for help
    3. 1. ChIP-seq – The Basics
    4. 1. ChIP-seq – The Basics ChIP-on-chip ChIP-seq
    5. 1. ChIP-seq – The Basics ChIP-on-chip Bioinformatics ChIP-seq
    6. 1. ChIP-seq – The Basics 35 bp 500 bp 35 bp
    7. 1. ChIP-seq – The Basics
    8. 2. Typical pipeline
    9. 2. Typical pipeline
    10. 2. Typical pipeline Bowtie
    11. 2. Typical pipeline Bowtie MACS
    12. 2. Typical pipeline Bowtie MACS CEAS
    13. 2. Typical pipeline Mapping… Unique / multiple locations Allowing mismatches – seed sequence Balance accuracy / performance Peak calling…
    14. 2. Typical pipeline
    15. 3. Challenges in ChIP-seq data analysis Millions of segments that need a fast mapping to the genome (allowing mismatches or gaps, performance issues) Peak detection – find the exact binding site Data normalization – compare results, background noise Visualization – thousands of enriched regions. UCSC, JBrowse…
    16. 4. To take into account Transcription Factors vs Nucleosomes / Histone modifications Control available? Sequencing depth bias in Control vs IP Different alignment methods produce different peak calling results, but the difference is not as much as the one due to different peak caller or replicate Many differences on peak callers can be explained by the different thresholds used Some peak callers may be specific to some data types Consistency may be used to set threshold if replicates are available
    17. 4. To take into account There are many tools for the analysis of ChIP- seq data, but no standards yet
    18. 5. Available tools
    19. 5. Available tools
    20. 5. Available tools
    21. 5. Available tools
    22. 5. Available tools Uses regional averaging to mitigate sample fluctuations in the control library Uses the control to model the distribution across the genome using the Poisson distribution (BG). After identifying candidate peaks significantly enriched over the BG, a local labda is estimated using windows around each peak to eliminate local biases Open-source, open to contributions (Artistic License) and being actively improved Easy to use and fast-responding developers Compares very well to other methods
    23. 6. Analysis example QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type) lane5_SNAIL_F9_qseq.txt SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1 @3_1_3_89 CACAGTGTCCTCCAGGTTCATCCC................ +3_1_3_89 @FC30C11AAXX:8:1:1649:1790 abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG @3_1_3_762 + GCAAACAAATGGCGGAAAGCGGCG................ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663 +3_1_3_762 @FC30C11AAXX:8:1:1655:1811 aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT @3_1_90_512 + GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT <<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+ +3_1_90_512 @FC30C11AAXX:8:1:1609:1848 ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT @3_1_90_1028 + GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG <<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%- +3_1_90_1028 @FC30C11AAXX:8:1:1667:1880 `bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA @3_1_90_1651 + TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566 +3_1_90_1651 @FC30C11AAXX:8:1:1577:1853 aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG @3_1_90_1670 + <6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044
    24. 6. Analysis example QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type) lane5_SNAIL_F9_qseq.txt SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1 Filter qualities and parse @3_1_3_89 CACAGTGTCCTCCAGGTTCATCCC................ +3_1_3_89 @FC30C11AAXX:8:1:1649:1790 abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG @3_1_3_762 + GCAAACAAATGGCGGAAAGCGGCG................ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663 +3_1_3_762 @FC30C11AAXX:8:1:1655:1811 aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT @3_1_90_512 + GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT <<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+ +3_1_90_512 @FC30C11AAXX:8:1:1609:1848 ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT @3_1_90_1028 + GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG <<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%- +3_1_90_1028 @FC30C11AAXX:8:1:1667:1880 `bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA @3_1_90_1651 + TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566 +3_1_90_1651 @FC30C11AAXX:8:1:1577:1853 aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG @3_1_90_1670 + <6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044
    25. 6. Analysis example QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type) lane5_SNAIL_F9_qseq.txt SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0 SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1 SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1 Filter qualities and parse @3_1_3_89 CACAGTGTCCTCCAGGTTCATCCC................ +3_1_3_89 @FC30C11AAXX:8:1:1649:1790 abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG @3_1_3_762 + GCAAACAAATGGCGGAAAGCGGCG................ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663 +3_1_3_762 @FC30C11AAXX:8:1:1655:1811 aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT @3_1_90_512 + GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT <<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+ +3_1_90_512 @FC30C11AAXX:8:1:1609:1848 ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY @3_1_90_1028 GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG BOWTIE GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT + <<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%- +3_1_90_1028 @FC30C11AAXX:8:1:1667:1880 `bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA @3_1_90_1651 + TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566 +3_1_90_1651 @FC30C11AAXX:8:1:1577:1853 aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG @3_1_90_1670 + <6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044
    26. 6. Analysis example SNAIL_F9.bwt 5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T 5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G 5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0 5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0 5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0 5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0 5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0 5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0 5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G
    27. 6. Analysis example SNAIL_F9.bwt 5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T 5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G 5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0 5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0 5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0 5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0 5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0 5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0 5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G Parsing SNAIL_F9.bwt.bed chr21 34604194 34604219 5_1_0_1409 . + chr23 77246408 77246434 5_1_0_811 . + chr02 201785208 201785237 5_1_1_1665 . + chr15 92942360 92942389 5_1_2_1637 . + chr03 101351498 101351527 5_1_2_1359 . + chr05 1314600 1314629 5_1_2_730 . - chr07 157199758 157199787 5_1_2_1118 . - chr11 133317176 133317208 5_1_3_920 . + chr12 7497006 7497038 5_1_3_971 . + chr01 201404048 201404081 5_1_3_1986 . +
    28. 6. Analysis example SNAIL_F9.bwt 5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T 5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G 5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0 5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0 5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0 5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0 5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0 5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0 5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G Parsing SNAIL_F9.bwt.bed chr21 34604194 34604219 5_1_0_1409 . + chr23 77246408 77246434 5_1_0_811 . + chr02 201785208 201785237 5_1_1_1665 . + chr15 92942360 92942389 5_1_2_1637 . + chr03 101351498 101351527 5_1_2_1359 . + chr05 1314600 1314629 5_1_2_730 . - chr07 chr11 157199758 133317176 157199787 133317208 5_1_2_1118 5_1_3_920 . . MACS - + chr12 7497006 7497038 5_1_3_971 . + chr01 201404048 201404081 5_1_3_1986 . +
    29. 6. Analysis example MACS pipeline Output: - Peak locations in BED and XLS format (genome browser) - Tag count in wiggle format (genome browser) - Bimodal model in R scripts
    30. 6. Analysis example H3K27me3 PolII
    31. 6. Analysis example snail_mfold_15_tsize41_newbwt_peaks.bed track name="MACS peaks for snail_mfold_15_tsize41_newbwt" chr1 559644 559924 MACS_peak_1 79.29 chr1 2435221 2435542 MACS_peak_2 51.58 chr1 14624217 14624571 MACS_peak_3 66.12 chr1 15610639 15611000 MACS_peak_4 56.69 chr1 16822564 16822753 MACS_peak_5 52.84 chr1 18411948 18412187 MACS_peak_6 82.46 chr1 22857612 22857985 MACS_peak_7 88.74 chr1 27541904 27542134 MACS_peak_8 69.47 snail_mfold_15_MACS.wig track type=wiggle_0 name="MACS_counts_after_shifting" description="Shifted Merged MACS tag counts for every 10 bp" variableStep chrom=chr10 span=10 85171 1 85181 1 85191 1 85201 1 85211 1 85221 1 85231 2 85371 2
    32. 6. Analysis example snail_mfold_15_tsize41_newbwt_peaks.bed track name="MACS peaks for snail_mfold_15_tsize41_newbwt" chr1 559644 559924 MACS_peak_1 79.29 chr1 2435221 2435542 MACS_peak_2 51.58 chr1 14624217 14624571 MACS_peak_3 66.12 chr1 15610639 15611000 MACS_peak_4 56.69 chr1 16822564 16822753 MACS_peak_5 52.84 chr1 18411948 18412187 MACS_peak_6 82.46 chr1 22857612 22857985 MACS_peak_7 88.74 chr1 27541904 27542134 MACS_peak_8 69.47 snail_mfold_15_MACS.wig track type=wiggle_0 name="MACS_counts_after_shifting" description="Shifted Merged MACS tag counts for every 10 bp" variableStep chrom=chr10 span=10 85171 1 85181 1 85191 1 85201 1 85211 1 CEAS 85221 1 85231 2 85371 2
    33. 6. Analysis example Input: -BED format peak locations - Optional signal profile in wiggle format - BED format extra regions of interest
    34. CEAS output
    35. CEAS output
    36. CEAS output
    37. CEAS output
    38. CEAS output
    39. CEAS output
    40. 7. Future challenges Re-analyze data with new algorithms – sequences remain the same ChIP-seq combined with Chromatin Conformation Capture (3C) – long-range physical interactions Technical improvements: RNA-seq will benefit from longer reads Integrated computational analyses – integration of TF, histone marks, methylation, polymerase loading to predict regulatory output
    41. 8. Where to look for help... Seqanswers.com
    42. 8. Where to look for help... Seqanswers.com Google groups, mailing lists of each project MACS CEAS FindPeaks
    43. 8. Where to look for help... Seqanswers.com Google groups, mailing lists of each project MACS CEAS FindPeaks Lab mates!
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